Overview

Dataset statistics

Number of variables99
Number of observations620
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory479.7 KiB
Average record size in memory792.2 B

Variable types

Numeric9
Categorical90

Warnings

index__LacticAcid_0 has constant value "0.0" Constant
index__timesincelast_0 has constant value "0.0" Constant
index__timesincestart_0 has constant value "0.0" Constant
static__DiagnosticArtAstrup_other is highly correlated with static__DiagnosticBlood_other and 23 other fieldsHigh correlation
static__DiagnosticBlood_True is highly correlated with static__DiagnosticIC_TrueHigh correlation
static__DiagnosticBlood_other is highly correlated with static__DiagnosticArtAstrup_other and 23 other fieldsHigh correlation
static__DiagnosticECG_other is highly correlated with static__DiagnosticArtAstrup_other and 23 other fieldsHigh correlation
static__DiagnosticIC_True is highly correlated with static__DiagnosticBlood_TrueHigh correlation
static__DiagnosticIC_other is highly correlated with static__DiagnosticArtAstrup_other and 23 other fieldsHigh correlation
static__DiagnosticLacticAcid_other is highly correlated with static__DiagnosticArtAstrup_other and 23 other fieldsHigh correlation
static__DiagnosticLiquor_False is highly correlated with static__DiagnosticArtAstrup_other and 21 other fieldsHigh correlation
static__DiagnosticLiquor_other is highly correlated with static__DiagnosticArtAstrup_other and 21 other fieldsHigh correlation
static__DiagnosticOther_False is highly correlated with static__DiagnosticArtAstrup_other and 21 other fieldsHigh correlation
static__DiagnosticOther_other is highly correlated with static__DiagnosticArtAstrup_other and 21 other fieldsHigh correlation
static__DiagnosticSputum_other is highly correlated with static__DiagnosticArtAstrup_other and 23 other fieldsHigh correlation
static__DiagnosticUrinaryCulture_other is highly correlated with static__DiagnosticArtAstrup_other and 23 other fieldsHigh correlation
static__DiagnosticUrinarySediment_other is highly correlated with static__DiagnosticArtAstrup_other and 23 other fieldsHigh correlation
static__DiagnosticXthorax_other is highly correlated with static__DiagnosticArtAstrup_other and 23 other fieldsHigh correlation
static__DisfuncOrg_other is highly correlated with static__DiagnosticArtAstrup_other and 23 other fieldsHigh correlation
static__Hypotensie_other is highly correlated with static__DiagnosticArtAstrup_other and 23 other fieldsHigh correlation
static__Hypoxie_other is highly correlated with static__DiagnosticArtAstrup_other and 23 other fieldsHigh correlation
static__InfectionSuspected_False is highly correlated with static__SIRSCriteria2OrMore_FalseHigh correlation
static__InfectionSuspected_True is highly correlated with static__SIRSCriteria2OrMore_TrueHigh correlation
static__InfectionSuspected_other is highly correlated with static__DiagnosticArtAstrup_other and 23 other fieldsHigh correlation
static__Infusion_other is highly correlated with static__DiagnosticArtAstrup_other and 23 other fieldsHigh correlation
static__Oligurie_other is highly correlated with static__DiagnosticArtAstrup_other and 23 other fieldsHigh correlation
static__SIRSCritHeartRate_other is highly correlated with static__DiagnosticArtAstrup_other and 23 other fieldsHigh correlation
static__SIRSCritLeucos_other is highly correlated with static__DiagnosticArtAstrup_other and 23 other fieldsHigh correlation
static__SIRSCritTachypnea_other is highly correlated with static__DiagnosticArtAstrup_other and 23 other fieldsHigh correlation
static__SIRSCritTemperature_other is highly correlated with static__DiagnosticArtAstrup_other and 23 other fieldsHigh correlation
static__SIRSCriteria2OrMore_False is highly correlated with static__InfectionSuspected_FalseHigh correlation
static__SIRSCriteria2OrMore_True is highly correlated with static__InfectionSuspected_TrueHigh correlation
static__SIRSCriteria2OrMore_other is highly correlated with static__DiagnosticArtAstrup_other and 23 other fieldsHigh correlation
index__hour_0 is highly correlated with index__timesincemidnight_0High correlation
index__timesincemidnight_0 is highly correlated with index__hour_0High correlation
index__concept:name_0_CRP is highly correlated with index__org:group_0_BHigh correlation
index__concept:name_0_ER Registration is highly correlated with static__DiagnosticArtAstrup_other and 23 other fieldsHigh correlation
index__concept:name_0_ER Triage is highly correlated with index__org:group_0_CHigh correlation
index__org:group_0_B is highly correlated with index__concept:name_0_CRPHigh correlation
index__org:group_0_C is highly correlated with index__concept:name_0_ER TriageHigh correlation
static__InfectionSuspected_True is highly correlated with index__LacticAcid_0 and 3 other fieldsHigh correlation
index__org:group_0_C is highly correlated with index__LacticAcid_0 and 2 other fieldsHigh correlation
index__org:group_0_A is highly correlated with index__LacticAcid_0 and 2 other fieldsHigh correlation
static__SIRSCritTemperature_False is highly correlated with index__LacticAcid_0 and 2 other fieldsHigh correlation
static__SIRSCritTemperature_True is highly correlated with index__LacticAcid_0 and 2 other fieldsHigh correlation
static__DiagnosticSputum_False is highly correlated with index__LacticAcid_0 and 2 other fieldsHigh correlation
static__Diagnose_N is highly correlated with index__LacticAcid_0 and 2 other fieldsHigh correlation
index__org:group_0_B is highly correlated with index__LacticAcid_0 and 2 other fieldsHigh correlation
static__Diagnose_B is highly correlated with index__LacticAcid_0 and 2 other fieldsHigh correlation
static__DisfuncOrg_other is highly correlated with index__LacticAcid_0 and 26 other fieldsHigh correlation
static__SIRSCritHeartRate_True is highly correlated with index__LacticAcid_0 and 2 other fieldsHigh correlation
index__LacticAcid_0 is highly correlated with static__InfectionSuspected_True and 88 other fieldsHigh correlation
index__concept:name_0_IV Liquid is highly correlated with index__LacticAcid_0 and 2 other fieldsHigh correlation
static__SIRSCriteria2OrMore_False is highly correlated with index__LacticAcid_0 and 3 other fieldsHigh correlation
static__Oligurie_True is highly correlated with index__LacticAcid_0 and 2 other fieldsHigh correlation
static__Diagnose_Q is highly correlated with index__LacticAcid_0 and 2 other fieldsHigh correlation
static__DiagnosticIC_False is highly correlated with index__LacticAcid_0 and 2 other fieldsHigh correlation
static__Diagnose_R is highly correlated with index__LacticAcid_0 and 2 other fieldsHigh correlation
index__concept:name_0_Leucocytes is highly correlated with index__LacticAcid_0 and 2 other fieldsHigh correlation
static__Diagnose_K is highly correlated with index__LacticAcid_0 and 2 other fieldsHigh correlation
index__OrderOfEvent_0 is highly correlated with index__LacticAcid_0 and 2 other fieldsHigh correlation
static__DiagnosticUrinarySediment_False is highly correlated with index__LacticAcid_0 and 2 other fieldsHigh correlation
static__Diagnose_S is highly correlated with index__LacticAcid_0 and 2 other fieldsHigh correlation
static__DiagnosticUrinarySediment_True is highly correlated with index__LacticAcid_0 and 2 other fieldsHigh correlation
static__DiagnosticArtAstrup_other is highly correlated with static__DisfuncOrg_other and 26 other fieldsHigh correlation
static__SIRSCritLeucos_other is highly correlated with static__DisfuncOrg_other and 26 other fieldsHigh correlation
static__Diagnose_other is highly correlated with index__LacticAcid_0 and 2 other fieldsHigh correlation
static__DiagnosticOther_other is highly correlated with static__DisfuncOrg_other and 24 other fieldsHigh correlation
static__DiagnosticIC_True is highly correlated with index__LacticAcid_0 and 3 other fieldsHigh correlation
static__DiagnosticXthorax_True is highly correlated with index__LacticAcid_0 and 2 other fieldsHigh correlation
static__Diagnose_G is highly correlated with index__LacticAcid_0 and 2 other fieldsHigh correlation
static__DiagnosticUrinaryCulture_False is highly correlated with index__LacticAcid_0 and 2 other fieldsHigh correlation
static__DiagnosticLacticAcid_True is highly correlated with index__LacticAcid_0 and 2 other fieldsHigh correlation
static__SIRSCritLeucos_True is highly correlated with index__LacticAcid_0 and 2 other fieldsHigh correlation
index__concept:name_0_ER Sepsis Triage is highly correlated with index__LacticAcid_0 and 2 other fieldsHigh correlation
static__DiagnosticUrinarySediment_other is highly correlated with static__DisfuncOrg_other and 26 other fieldsHigh correlation
static__SIRSCriteria2OrMore_True is highly correlated with static__InfectionSuspected_True and 3 other fieldsHigh correlation
static__DiagnosticArtAstrup_False is highly correlated with index__LacticAcid_0 and 2 other fieldsHigh correlation
static__DiagnosticBlood_True is highly correlated with index__LacticAcid_0 and 3 other fieldsHigh correlation
static__DiagnosticOther_False is highly correlated with static__DisfuncOrg_other and 24 other fieldsHigh correlation
static__SIRSCriteria2OrMore_other is highly correlated with static__DisfuncOrg_other and 26 other fieldsHigh correlation
static__Hypotensie_True is highly correlated with index__LacticAcid_0 and 2 other fieldsHigh correlation
static__SIRSCritLeucos_False is highly correlated with index__LacticAcid_0 and 2 other fieldsHigh correlation
static__Infusion_True is highly correlated with index__LacticAcid_0 and 2 other fieldsHigh correlation
static__DiagnosticSputum_other is highly correlated with static__DisfuncOrg_other and 26 other fieldsHigh correlation
static__Hypotensie_other is highly correlated with static__DisfuncOrg_other and 26 other fieldsHigh correlation
static__Hypoxie_other is highly correlated with static__DisfuncOrg_other and 26 other fieldsHigh correlation
static__DiagnosticBlood_other is highly correlated with static__DisfuncOrg_other and 26 other fieldsHigh correlation
static__SIRSCritTemperature_other is highly correlated with static__DisfuncOrg_other and 26 other fieldsHigh correlation
index__timesincelast_0 is highly correlated with static__InfectionSuspected_True and 88 other fieldsHigh correlation
index__concept:name_0_ER Registration is highly correlated with static__DisfuncOrg_other and 26 other fieldsHigh correlation
static__Infusion_False is highly correlated with index__LacticAcid_0 and 2 other fieldsHigh correlation
static__DisfuncOrg_False is highly correlated with index__LacticAcid_0 and 2 other fieldsHigh correlation
index__concept:name_0_CRP is highly correlated with index__LacticAcid_0 and 2 other fieldsHigh correlation
static__DiagnosticLiquor_other is highly correlated with static__DisfuncOrg_other and 24 other fieldsHigh correlation
static__Hypoxie_True is highly correlated with index__LacticAcid_0 and 2 other fieldsHigh correlation
static__DiagnosticSputum_True is highly correlated with index__LacticAcid_0 and 2 other fieldsHigh correlation
static__DiagnosticECG_True is highly correlated with index__LacticAcid_0 and 2 other fieldsHigh correlation
static__DiagnosticUrinaryCulture_other is highly correlated with static__DisfuncOrg_other and 26 other fieldsHigh correlation
static__SIRSCritHeartRate_other is highly correlated with static__DisfuncOrg_other and 26 other fieldsHigh correlation
static__DiagnosticBlood_False is highly correlated with index__LacticAcid_0 and 2 other fieldsHigh correlation
static__Infusion_other is highly correlated with static__DisfuncOrg_other and 26 other fieldsHigh correlation
static__DiagnosticLacticAcid_other is highly correlated with static__DisfuncOrg_other and 26 other fieldsHigh correlation
static__InfectionSuspected_False is highly correlated with index__LacticAcid_0 and 3 other fieldsHigh correlation
static__Hypotensie_False is highly correlated with index__LacticAcid_0 and 2 other fieldsHigh correlation
static__Oligurie_other is highly correlated with static__DisfuncOrg_other and 26 other fieldsHigh correlation
index__org:group_0_L is highly correlated with index__LacticAcid_0 and 2 other fieldsHigh correlation
static__DiagnosticLacticAcid_False is highly correlated with index__LacticAcid_0 and 2 other fieldsHigh correlation
index__timesincestart_0 is highly correlated with static__InfectionSuspected_True and 88 other fieldsHigh correlation
static__DiagnosticXthorax_False is highly correlated with index__LacticAcid_0 and 2 other fieldsHigh correlation
static__DiagnosticECG_False is highly correlated with index__LacticAcid_0 and 2 other fieldsHigh correlation
static__DisfuncOrg_True is highly correlated with index__LacticAcid_0 and 2 other fieldsHigh correlation
static__DiagnosticECG_other is highly correlated with static__DisfuncOrg_other and 26 other fieldsHigh correlation
static__Diagnose_C is highly correlated with index__LacticAcid_0 and 2 other fieldsHigh correlation
static__SIRSCritHeartRate_False is highly correlated with index__LacticAcid_0 and 2 other fieldsHigh correlation
index__concept:name_0_ER Triage is highly correlated with index__LacticAcid_0 and 2 other fieldsHigh correlation
static__SIRSCritTachypnea_False is highly correlated with index__LacticAcid_0 and 2 other fieldsHigh correlation
static__DiagnosticArtAstrup_True is highly correlated with index__LacticAcid_0 and 2 other fieldsHigh correlation
static__Diagnose_E is highly correlated with index__LacticAcid_0 and 2 other fieldsHigh correlation
static__Hypoxie_False is highly correlated with index__LacticAcid_0 and 2 other fieldsHigh correlation
static__Diagnose_D is highly correlated with index__LacticAcid_0 and 2 other fieldsHigh correlation
static__SIRSCritTachypnea_True is highly correlated with index__LacticAcid_0 and 2 other fieldsHigh correlation
static__DiagnosticIC_other is highly correlated with static__DisfuncOrg_other and 26 other fieldsHigh correlation
static__Oligurie_False is highly correlated with index__LacticAcid_0 and 2 other fieldsHigh correlation
static__DiagnosticLiquor_False is highly correlated with static__DisfuncOrg_other and 24 other fieldsHigh correlation
static__InfectionSuspected_other is highly correlated with static__DisfuncOrg_other and 26 other fieldsHigh correlation
static__SIRSCritTachypnea_other is highly correlated with static__DisfuncOrg_other and 26 other fieldsHigh correlation
static__DiagnosticUrinaryCulture_True is highly correlated with index__LacticAcid_0 and 2 other fieldsHigh correlation
static__DiagnosticXthorax_other is highly correlated with static__DisfuncOrg_other and 26 other fieldsHigh correlation
static__Diagnose_H is highly correlated with index__LacticAcid_0 and 2 other fieldsHigh correlation
index__remainingtime_0 has unique values Unique
index__CRP_0 has 606 (97.7%) zeros Zeros
index__Leucocytes_0 has 609 (98.2%) zeros Zeros
index__hour_0 has 12 (1.9%) zeros Zeros

Reproduction

Analysis started2021-04-14 08:04:05.177126
Analysis finished2021-04-14 08:05:11.462097
Duration1 minute and 6.28 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

static__Age
Real number (ℝ≥0)

Distinct15
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.63709677
Minimum20
Maximum90
Zeros0
Zeros (%)0.0%
Memory size5.0 KiB
2021-04-14T10:05:11.559716image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile40
Q160
median75
Q385
95-th percentile90
Maximum90
Range70
Interquartile range (IQR)25

Descriptive statistics

Standard deviation15.55454973
Coefficient of variation (CV)0.2171298173
Kurtosis0.4314472602
Mean71.63709677
Median Absolute Deviation (MAD)10
Skewness-0.9278159831
Sum44415
Variance241.9440174
MonotocityNot monotonic
2021-04-14T10:05:11.634051image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
9094
15.2%
8590
14.5%
8086
13.9%
7573
11.8%
7068
11.0%
6550
8.1%
6048
7.7%
5539
6.3%
5021
 
3.4%
4514
 
2.3%
Other values (5)37
 
6.0%
ValueCountFrequency (%)
203
 
0.5%
255
 
0.8%
306
1.0%
3510
1.6%
4013
2.1%
ValueCountFrequency (%)
9094
15.2%
8590
14.5%
8086
13.9%
7573
11.8%
7068
11.0%

static__Diagnose_B
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
568 
1.0
 
52

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0
ValueCountFrequency (%)
0.0568
91.6%
1.052
 
8.4%
2021-04-14T10:05:11.817489image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:11.870512image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0568
91.6%
1.052
 
8.4%

Most occurring characters

ValueCountFrequency (%)
01188
63.9%
.620
33.3%
152
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01188
95.8%
152
 
4.2%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01188
63.9%
.620
33.3%
152
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01188
63.9%
.620
33.3%
152
 
2.8%

static__Diagnose_C
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
494 
1.0
126 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0494
79.7%
1.0126
 
20.3%
2021-04-14T10:05:12.013026image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:12.065765image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0494
79.7%
1.0126
 
20.3%

Most occurring characters

ValueCountFrequency (%)
01114
59.9%
.620
33.3%
1126
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01114
89.8%
1126
 
10.2%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01114
59.9%
.620
33.3%
1126
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01114
59.9%
.620
33.3%
1126
 
6.8%

static__Diagnose_D
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
600 
1.0
 
20

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0600
96.8%
1.020
 
3.2%
2021-04-14T10:05:12.203902image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:12.256973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0600
96.8%
1.020
 
3.2%

Most occurring characters

ValueCountFrequency (%)
01220
65.6%
.620
33.3%
120
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01220
98.4%
120
 
1.6%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01220
65.6%
.620
33.3%
120
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01220
65.6%
.620
33.3%
120
 
1.1%

static__Diagnose_E
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
576 
1.0
 
44

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0576
92.9%
1.044
 
7.1%
2021-04-14T10:05:12.392280image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:12.445538image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0576
92.9%
1.044
 
7.1%

Most occurring characters

ValueCountFrequency (%)
01196
64.3%
.620
33.3%
144
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01196
96.5%
144
 
3.5%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01196
64.3%
.620
33.3%
144
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01196
64.3%
.620
33.3%
144
 
2.4%

static__Diagnose_G
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
588 
1.0
 
32

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0588
94.8%
1.032
 
5.2%
2021-04-14T10:05:12.696937image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:12.750015image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0588
94.8%
1.032
 
5.2%

Most occurring characters

ValueCountFrequency (%)
01208
64.9%
.620
33.3%
132
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01208
97.4%
132
 
2.6%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01208
64.9%
.620
33.3%
132
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01208
64.9%
.620
33.3%
132
 
1.7%

static__Diagnose_H
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
580 
1.0
 
40

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0580
93.5%
1.040
 
6.5%
2021-04-14T10:05:12.885356image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:12.938300image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0580
93.5%
1.040
 
6.5%

Most occurring characters

ValueCountFrequency (%)
01200
64.5%
.620
33.3%
140
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01200
96.8%
140
 
3.2%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01200
64.5%
.620
33.3%
140
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01200
64.5%
.620
33.3%
140
 
2.2%

static__Diagnose_K
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
604 
1.0
 
16

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0604
97.4%
1.016
 
2.6%
2021-04-14T10:05:13.076321image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:13.129079image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0604
97.4%
1.016
 
2.6%

Most occurring characters

ValueCountFrequency (%)
01224
65.8%
.620
33.3%
116
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01224
98.7%
116
 
1.3%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01224
65.8%
.620
33.3%
116
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01224
65.8%
.620
33.3%
116
 
0.9%

static__Diagnose_N
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
612 
1.0
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0612
98.7%
1.08
 
1.3%
2021-04-14T10:05:13.267206image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:13.319963image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0612
98.7%
1.08
 
1.3%

Most occurring characters

ValueCountFrequency (%)
01232
66.2%
.620
33.3%
18
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01232
99.4%
18
 
0.6%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01232
66.2%
.620
33.3%
18
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01232
66.2%
.620
33.3%
18
 
0.4%

static__Diagnose_Q
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
612 
1.0
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0612
98.7%
1.08
 
1.3%
2021-04-14T10:05:13.457901image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:13.510712image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0612
98.7%
1.08
 
1.3%

Most occurring characters

ValueCountFrequency (%)
01232
66.2%
.620
33.3%
18
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01232
99.4%
18
 
0.6%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01232
66.2%
.620
33.3%
18
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01232
66.2%
.620
33.3%
18
 
0.4%

static__Diagnose_R
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
606 
1.0
 
14

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0606
97.7%
1.014
 
2.3%
2021-04-14T10:05:13.648714image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:13.701520image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0606
97.7%
1.014
 
2.3%

Most occurring characters

ValueCountFrequency (%)
01226
65.9%
.620
33.3%
114
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01226
98.9%
114
 
1.1%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01226
65.9%
.620
33.3%
114
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01226
65.9%
.620
33.3%
114
 
0.8%

static__Diagnose_S
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
609 
1.0
 
11

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0
ValueCountFrequency (%)
0.0609
98.2%
1.011
 
1.8%
2021-04-14T10:05:13.839174image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:13.891948image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0609
98.2%
1.011
 
1.8%

Most occurring characters

ValueCountFrequency (%)
01229
66.1%
.620
33.3%
111
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01229
99.1%
111
 
0.9%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01229
66.1%
.620
33.3%
111
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01229
66.1%
.620
33.3%
111
 
0.6%

static__Diagnose_other
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
371 
1.0
249 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0371
59.8%
1.0249
40.2%
2021-04-14T10:05:14.027005image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:14.079777image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0371
59.8%
1.0249
40.2%

Most occurring characters

ValueCountFrequency (%)
0991
53.3%
.620
33.3%
1249
 
13.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
0991
79.9%
1249
 
20.1%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
0991
53.3%
.620
33.3%
1249
 
13.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
0991
53.3%
.620
33.3%
1249
 
13.4%

static__DiagnosticArtAstrup_False
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
1.0
407 
0.0
213 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0
ValueCountFrequency (%)
1.0407
65.6%
0.0213
34.4%
2021-04-14T10:05:14.222391image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:14.275225image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0407
65.6%
0.0213
34.4%

Most occurring characters

ValueCountFrequency (%)
0833
44.8%
.620
33.3%
1407
21.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
0833
67.2%
1407
32.8%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
0833
44.8%
.620
33.3%
1407
21.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
0833
44.8%
.620
33.3%
1407
21.9%

static__DiagnosticArtAstrup_True
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
441 
1.0
179 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0441
71.1%
1.0179
28.9%
2021-04-14T10:05:14.410260image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:14.463044image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0441
71.1%
1.0179
28.9%

Most occurring characters

ValueCountFrequency (%)
01061
57.0%
.620
33.3%
1179
 
9.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01061
85.6%
1179
 
14.4%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01061
57.0%
.620
33.3%
1179
 
9.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01061
57.0%
.620
33.3%
1179
 
9.6%

static__DiagnosticArtAstrup_other
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
586 
1.0
 
34

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0586
94.5%
1.034
 
5.5%
2021-04-14T10:05:14.598544image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:14.651393image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0586
94.5%
1.034
 
5.5%

Most occurring characters

ValueCountFrequency (%)
01206
64.8%
.620
33.3%
134
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01206
97.3%
134
 
2.7%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01206
64.8%
.620
33.3%
134
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01206
64.8%
.620
33.3%
134
 
1.8%

static__DiagnosticBlood_False
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
532 
1.0
88 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0
ValueCountFrequency (%)
0.0532
85.8%
1.088
 
14.2%
2021-04-14T10:05:14.901901image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:14.954824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0532
85.8%
1.088
 
14.2%

Most occurring characters

ValueCountFrequency (%)
01152
61.9%
.620
33.3%
188
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01152
92.9%
188
 
7.1%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01152
61.9%
.620
33.3%
188
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01152
61.9%
.620
33.3%
188
 
4.7%

static__DiagnosticBlood_True
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
1.0
498 
0.0
122 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row0.0
5th row1.0
ValueCountFrequency (%)
1.0498
80.3%
0.0122
 
19.7%
2021-04-14T10:05:15.097074image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:15.149819image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0498
80.3%
0.0122
 
19.7%

Most occurring characters

ValueCountFrequency (%)
0742
39.9%
.620
33.3%
1498
26.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
0742
59.8%
1498
40.2%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
0742
39.9%
.620
33.3%
1498
26.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
0742
39.9%
.620
33.3%
1498
26.8%

static__DiagnosticBlood_other
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
586 
1.0
 
34

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0586
94.5%
1.034
 
5.5%
2021-04-14T10:05:15.284833image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:15.337526image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0586
94.5%
1.034
 
5.5%

Most occurring characters

ValueCountFrequency (%)
01206
64.8%
.620
33.3%
134
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01206
97.3%
134
 
2.7%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01206
64.8%
.620
33.3%
134
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01206
64.8%
.620
33.3%
134
 
1.8%

static__DiagnosticECG_False
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
497 
1.0
123 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0497
80.2%
1.0123
 
19.8%
2021-04-14T10:05:15.480003image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:15.532904image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0497
80.2%
1.0123
 
19.8%

Most occurring characters

ValueCountFrequency (%)
01117
60.1%
.620
33.3%
1123
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01117
90.1%
1123
 
9.9%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01117
60.1%
.620
33.3%
1123
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01117
60.1%
.620
33.3%
1123
 
6.6%

static__DiagnosticECG_True
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
1.0
463 
0.0
157 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0
ValueCountFrequency (%)
1.0463
74.7%
0.0157
 
25.3%
2021-04-14T10:05:15.671804image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:15.724478image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0463
74.7%
0.0157
 
25.3%

Most occurring characters

ValueCountFrequency (%)
0777
41.8%
.620
33.3%
1463
24.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
0777
62.7%
1463
37.3%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
0777
41.8%
.620
33.3%
1463
24.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
0777
41.8%
.620
33.3%
1463
24.9%

static__DiagnosticECG_other
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
586 
1.0
 
34

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0586
94.5%
1.034
 
5.5%
2021-04-14T10:05:15.859539image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:15.912352image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0586
94.5%
1.034
 
5.5%

Most occurring characters

ValueCountFrequency (%)
01206
64.8%
.620
33.3%
134
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01206
97.3%
134
 
2.7%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01206
64.8%
.620
33.3%
134
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01206
64.8%
.620
33.3%
134
 
1.8%

static__DiagnosticIC_False
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
547 
1.0
73 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0547
88.2%
1.073
 
11.8%
2021-04-14T10:05:16.043833image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:16.096614image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0547
88.2%
1.073
 
11.8%

Most occurring characters

ValueCountFrequency (%)
01167
62.7%
.620
33.3%
173
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01167
94.1%
173
 
5.9%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01167
62.7%
.620
33.3%
173
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01167
62.7%
.620
33.3%
173
 
3.9%

static__DiagnosticIC_True
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
1.0
513 
0.0
107 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0
ValueCountFrequency (%)
1.0513
82.7%
0.0107
 
17.3%
2021-04-14T10:05:16.242973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:16.295782image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0513
82.7%
0.0107
 
17.3%

Most occurring characters

ValueCountFrequency (%)
0727
39.1%
.620
33.3%
1513
27.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
0727
58.6%
1513
41.4%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
0727
39.1%
.620
33.3%
1513
27.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
0727
39.1%
.620
33.3%
1513
27.6%

static__DiagnosticIC_other
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
586 
1.0
 
34

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0586
94.5%
1.034
 
5.5%
2021-04-14T10:05:16.430807image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:16.483653image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0586
94.5%
1.034
 
5.5%

Most occurring characters

ValueCountFrequency (%)
01206
64.8%
.620
33.3%
134
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01206
97.3%
134
 
2.7%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01206
64.8%
.620
33.3%
134
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01206
64.8%
.620
33.3%
134
 
1.8%

static__DiagnosticLacticAcid_False
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
522 
1.0
98 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0522
84.2%
1.098
 
15.8%
2021-04-14T10:05:16.615330image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:16.669190image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0522
84.2%
1.098
 
15.8%

Most occurring characters

ValueCountFrequency (%)
01142
61.4%
.620
33.3%
198
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01142
92.1%
198
 
7.9%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01142
61.4%
.620
33.3%
198
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01142
61.4%
.620
33.3%
198
 
5.3%

static__DiagnosticLacticAcid_True
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
1.0
488 
0.0
132 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0
ValueCountFrequency (%)
1.0488
78.7%
0.0132
 
21.3%
2021-04-14T10:05:16.811761image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:16.864909image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0488
78.7%
0.0132
 
21.3%

Most occurring characters

ValueCountFrequency (%)
0752
40.4%
.620
33.3%
1488
26.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
0752
60.6%
1488
39.4%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
0752
40.4%
.620
33.3%
1488
26.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
0752
40.4%
.620
33.3%
1488
26.2%

static__DiagnosticLacticAcid_other
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
586 
1.0
 
34

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0586
94.5%
1.034
 
5.5%
2021-04-14T10:05:17.121654image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:17.174626image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0586
94.5%
1.034
 
5.5%

Most occurring characters

ValueCountFrequency (%)
01206
64.8%
.620
33.3%
134
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01206
97.3%
134
 
2.7%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01206
64.8%
.620
33.3%
134
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01206
64.8%
.620
33.3%
134
 
1.8%

static__DiagnosticLiquor_False
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
1.0
581 
0.0
 
39

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0
ValueCountFrequency (%)
1.0581
93.7%
0.039
 
6.3%
2021-04-14T10:05:17.309472image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:17.362196image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0581
93.7%
0.039
 
6.3%

Most occurring characters

ValueCountFrequency (%)
0659
35.4%
.620
33.3%
1581
31.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
0659
53.1%
1581
46.9%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
0659
35.4%
.620
33.3%
1581
31.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
0659
35.4%
.620
33.3%
1581
31.2%

static__DiagnosticLiquor_other
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
581 
1.0
 
39

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0581
93.7%
1.039
 
6.3%
2021-04-14T10:05:17.497137image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:17.549920image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0581
93.7%
1.039
 
6.3%

Most occurring characters

ValueCountFrequency (%)
01201
64.6%
.620
33.3%
139
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01201
96.9%
139
 
3.1%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01201
64.6%
.620
33.3%
139
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01201
64.6%
.620
33.3%
139
 
2.1%

static__DiagnosticOther_False
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
1.0
583 
0.0
 
37

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0
ValueCountFrequency (%)
1.0583
94.0%
0.037
 
6.0%
2021-04-14T10:05:17.685124image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:17.737929image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0583
94.0%
0.037
 
6.0%

Most occurring characters

ValueCountFrequency (%)
0657
35.3%
.620
33.3%
1583
31.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
0657
53.0%
1583
47.0%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
0657
35.3%
.620
33.3%
1583
31.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
0657
35.3%
.620
33.3%
1583
31.3%

static__DiagnosticOther_other
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
583 
1.0
 
37

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0583
94.0%
1.037
 
6.0%
2021-04-14T10:05:17.873004image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:17.925753image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0583
94.0%
1.037
 
6.0%

Most occurring characters

ValueCountFrequency (%)
01203
64.7%
.620
33.3%
137
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01203
97.0%
137
 
3.0%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01203
64.7%
.620
33.3%
137
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01203
64.7%
.620
33.3%
137
 
2.0%

static__DiagnosticSputum_False
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
1.0
568 
0.0
 
52

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0
ValueCountFrequency (%)
1.0568
91.6%
0.052
 
8.4%
2021-04-14T10:05:18.057384image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:18.110217image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0568
91.6%
0.052
 
8.4%

Most occurring characters

ValueCountFrequency (%)
0672
36.1%
.620
33.3%
1568
30.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
0672
54.2%
1568
45.8%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
0672
36.1%
.620
33.3%
1568
30.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
0672
36.1%
.620
33.3%
1568
30.5%

static__DiagnosticSputum_True
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
602 
1.0
 
18

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0602
97.1%
1.018
 
2.9%
2021-04-14T10:05:18.248224image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:18.301067image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0602
97.1%
1.018
 
2.9%

Most occurring characters

ValueCountFrequency (%)
01222
65.7%
.620
33.3%
118
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01222
98.5%
118
 
1.5%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01222
65.7%
.620
33.3%
118
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01222
65.7%
.620
33.3%
118
 
1.0%

static__DiagnosticSputum_other
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
586 
1.0
 
34

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0586
94.5%
1.034
 
5.5%
2021-04-14T10:05:18.435991image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:18.488939image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0586
94.5%
1.034
 
5.5%

Most occurring characters

ValueCountFrequency (%)
01206
64.8%
.620
33.3%
134
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01206
97.3%
134
 
2.7%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01206
64.8%
.620
33.3%
134
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01206
64.8%
.620
33.3%
134
 
1.8%

static__DiagnosticUrinaryCulture_False
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
330 
1.0
290 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0
ValueCountFrequency (%)
0.0330
53.2%
1.0290
46.8%
2021-04-14T10:05:18.635773image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:18.688555image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0330
53.2%
1.0290
46.8%

Most occurring characters

ValueCountFrequency (%)
0950
51.1%
.620
33.3%
1290
 
15.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
0950
76.6%
1290
 
23.4%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
0950
51.1%
.620
33.3%
1290
 
15.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
0950
51.1%
.620
33.3%
1290
 
15.6%

static__DiagnosticUrinaryCulture_True
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
324 
1.0
296 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row0.0
5th row1.0
ValueCountFrequency (%)
0.0324
52.3%
1.0296
47.7%
2021-04-14T10:05:18.828122image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:18.880992image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0324
52.3%
1.0296
47.7%

Most occurring characters

ValueCountFrequency (%)
0944
50.8%
.620
33.3%
1296
 
15.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
0944
76.1%
1296
 
23.9%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
0944
50.8%
.620
33.3%
1296
 
15.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
0944
50.8%
.620
33.3%
1296
 
15.9%

static__DiagnosticUrinaryCulture_other
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
586 
1.0
 
34

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0586
94.5%
1.034
 
5.5%
2021-04-14T10:05:19.016318image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:19.069414image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0586
94.5%
1.034
 
5.5%

Most occurring characters

ValueCountFrequency (%)
01206
64.8%
.620
33.3%
134
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01206
97.3%
134
 
2.7%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01206
64.8%
.620
33.3%
134
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01206
64.8%
.620
33.3%
134
 
1.8%

static__DiagnosticUrinarySediment_False
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
352 
1.0
268 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0352
56.8%
1.0268
43.2%
2021-04-14T10:05:19.322077image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:19.375066image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0352
56.8%
1.0268
43.2%

Most occurring characters

ValueCountFrequency (%)
0972
52.3%
.620
33.3%
1268
 
14.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
0972
78.4%
1268
 
21.6%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
0972
52.3%
.620
33.3%
1268
 
14.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
0972
52.3%
.620
33.3%
1268
 
14.4%

static__DiagnosticUrinarySediment_True
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
1.0
318 
0.0
302 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0
ValueCountFrequency (%)
1.0318
51.3%
0.0302
48.7%
2021-04-14T10:05:19.522032image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:19.574779image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0318
51.3%
0.0302
48.7%

Most occurring characters

ValueCountFrequency (%)
0922
49.6%
.620
33.3%
1318
 
17.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
0922
74.4%
1318
 
25.6%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
0922
49.6%
.620
33.3%
1318
 
17.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
0922
49.6%
.620
33.3%
1318
 
17.1%

static__DiagnosticUrinarySediment_other
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
586 
1.0
 
34

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0586
94.5%
1.034
 
5.5%
2021-04-14T10:05:19.709787image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:19.762442image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0586
94.5%
1.034
 
5.5%

Most occurring characters

ValueCountFrequency (%)
01206
64.8%
.620
33.3%
134
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01206
97.3%
134
 
2.7%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01206
64.8%
.620
33.3%
134
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01206
64.8%
.620
33.3%
134
 
1.8%

static__DiagnosticXthorax_False
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
511 
1.0
109 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0511
82.4%
1.0109
 
17.6%
2021-04-14T10:05:19.908718image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:19.961742image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0511
82.4%
1.0109
 
17.6%

Most occurring characters

ValueCountFrequency (%)
01131
60.8%
.620
33.3%
1109
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01131
91.2%
1109
 
8.8%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01131
60.8%
.620
33.3%
1109
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01131
60.8%
.620
33.3%
1109
 
5.9%

static__DiagnosticXthorax_True
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
1.0
477 
0.0
143 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0
ValueCountFrequency (%)
1.0477
76.9%
0.0143
 
23.1%
2021-04-14T10:05:20.100525image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:20.153623image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0477
76.9%
0.0143
 
23.1%

Most occurring characters

ValueCountFrequency (%)
0763
41.0%
.620
33.3%
1477
25.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
0763
61.5%
1477
38.5%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
0763
41.0%
.620
33.3%
1477
25.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
0763
41.0%
.620
33.3%
1477
25.6%

static__DiagnosticXthorax_other
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
586 
1.0
 
34

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0586
94.5%
1.034
 
5.5%
2021-04-14T10:05:20.289158image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:20.342220image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0586
94.5%
1.034
 
5.5%

Most occurring characters

ValueCountFrequency (%)
01206
64.8%
.620
33.3%
134
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01206
97.3%
134
 
2.7%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01206
64.8%
.620
33.3%
134
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01206
64.8%
.620
33.3%
134
 
1.8%

static__DisfuncOrg_False
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
1.0
547 
0.0
73 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0
ValueCountFrequency (%)
1.0547
88.2%
0.073
 
11.8%
2021-04-14T10:05:20.474265image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:20.527454image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0547
88.2%
0.073
 
11.8%

Most occurring characters

ValueCountFrequency (%)
0693
37.3%
.620
33.3%
1547
29.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
0693
55.9%
1547
44.1%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
0693
37.3%
.620
33.3%
1547
29.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
0693
37.3%
.620
33.3%
1547
29.4%

static__DisfuncOrg_True
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
581 
1.0
 
39

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0581
93.7%
1.039
 
6.3%
2021-04-14T10:05:20.663592image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:20.717201image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0581
93.7%
1.039
 
6.3%

Most occurring characters

ValueCountFrequency (%)
01201
64.6%
.620
33.3%
139
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01201
96.9%
139
 
3.1%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01201
64.6%
.620
33.3%
139
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01201
64.6%
.620
33.3%
139
 
2.1%

static__DisfuncOrg_other
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
586 
1.0
 
34

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0586
94.5%
1.034
 
5.5%
2021-04-14T10:05:20.853008image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:20.906041image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0586
94.5%
1.034
 
5.5%

Most occurring characters

ValueCountFrequency (%)
01206
64.8%
.620
33.3%
134
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01206
97.3%
134
 
2.7%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01206
64.8%
.620
33.3%
134
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01206
64.8%
.620
33.3%
134
 
1.8%

static__Hypotensie_False
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
1.0
550 
0.0
70 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0
ValueCountFrequency (%)
1.0550
88.7%
0.070
 
11.3%
2021-04-14T10:05:21.038153image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:21.091185image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0550
88.7%
0.070
 
11.3%

Most occurring characters

ValueCountFrequency (%)
0690
37.1%
.620
33.3%
1550
29.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
0690
55.6%
1550
44.4%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
0690
37.1%
.620
33.3%
1550
29.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
0690
37.1%
.620
33.3%
1550
29.6%

static__Hypotensie_True
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
584 
1.0
 
36

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0584
94.2%
1.036
 
5.8%
2021-04-14T10:05:21.226773image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:21.279586image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0584
94.2%
1.036
 
5.8%

Most occurring characters

ValueCountFrequency (%)
01204
64.7%
.620
33.3%
136
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01204
97.1%
136
 
2.9%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01204
64.7%
.620
33.3%
136
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01204
64.7%
.620
33.3%
136
 
1.9%

static__Hypotensie_other
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
586 
1.0
 
34

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0586
94.5%
1.034
 
5.5%
2021-04-14T10:05:21.414891image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:21.592218image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0586
94.5%
1.034
 
5.5%

Most occurring characters

ValueCountFrequency (%)
01206
64.8%
.620
33.3%
134
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01206
97.3%
134
 
2.7%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01206
64.8%
.620
33.3%
134
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01206
64.8%
.620
33.3%
134
 
1.8%

static__Hypoxie_False
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
1.0
574 
0.0
 
46

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0
ValueCountFrequency (%)
1.0574
92.6%
0.046
 
7.4%
2021-04-14T10:05:21.728091image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:21.780951image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0574
92.6%
0.046
 
7.4%

Most occurring characters

ValueCountFrequency (%)
0666
35.8%
.620
33.3%
1574
30.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
0666
53.7%
1574
46.3%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
0666
35.8%
.620
33.3%
1574
30.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
0666
35.8%
.620
33.3%
1574
30.9%

static__Hypoxie_True
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
608 
1.0
 
12

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0608
98.1%
1.012
 
1.9%
2021-04-14T10:05:21.918902image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:21.971767image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0608
98.1%
1.012
 
1.9%

Most occurring characters

ValueCountFrequency (%)
01228
66.0%
.620
33.3%
112
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01228
99.0%
112
 
1.0%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01228
66.0%
.620
33.3%
112
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01228
66.0%
.620
33.3%
112
 
0.6%

static__Hypoxie_other
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
586 
1.0
 
34

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0586
94.5%
1.034
 
5.5%
2021-04-14T10:05:22.106735image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:22.159581image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0586
94.5%
1.034
 
5.5%

Most occurring characters

ValueCountFrequency (%)
01206
64.8%
.620
33.3%
134
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01206
97.3%
134
 
2.7%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01206
64.8%
.620
33.3%
134
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01206
64.8%
.620
33.3%
134
 
1.8%

static__InfectionSuspected_False
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
547 
1.0
73 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0547
88.2%
1.073
 
11.8%
2021-04-14T10:05:22.290947image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:22.343784image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0547
88.2%
1.073
 
11.8%

Most occurring characters

ValueCountFrequency (%)
01167
62.7%
.620
33.3%
173
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01167
94.1%
173
 
5.9%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01167
62.7%
.620
33.3%
173
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01167
62.7%
.620
33.3%
173
 
3.9%

static__InfectionSuspected_True
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
1.0
513 
0.0
107 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0
ValueCountFrequency (%)
1.0513
82.7%
0.0107
 
17.3%
2021-04-14T10:05:22.490333image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:22.543184image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0513
82.7%
0.0107
 
17.3%

Most occurring characters

ValueCountFrequency (%)
0727
39.1%
.620
33.3%
1513
27.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
0727
58.6%
1513
41.4%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
0727
39.1%
.620
33.3%
1513
27.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
0727
39.1%
.620
33.3%
1513
27.6%

static__InfectionSuspected_other
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
586 
1.0
 
34

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0586
94.5%
1.034
 
5.5%
2021-04-14T10:05:22.678438image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:22.731276image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0586
94.5%
1.034
 
5.5%

Most occurring characters

ValueCountFrequency (%)
01206
64.8%
.620
33.3%
134
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01206
97.3%
134
 
2.7%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01206
64.8%
.620
33.3%
134
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01206
64.8%
.620
33.3%
134
 
1.8%

static__Infusion_False
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
525 
1.0
95 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0
ValueCountFrequency (%)
0.0525
84.7%
1.095
 
15.3%
2021-04-14T10:05:22.862713image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:22.915637image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0525
84.7%
1.095
 
15.3%

Most occurring characters

ValueCountFrequency (%)
01145
61.6%
.620
33.3%
195
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01145
92.3%
195
 
7.7%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01145
61.6%
.620
33.3%
195
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01145
61.6%
.620
33.3%
195
 
5.1%

static__Infusion_True
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
1.0
491 
0.0
129 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row0.0
ValueCountFrequency (%)
1.0491
79.2%
0.0129
 
20.8%
2021-04-14T10:05:23.058094image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:23.111042image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0491
79.2%
0.0129
 
20.8%

Most occurring characters

ValueCountFrequency (%)
0749
40.3%
.620
33.3%
1491
26.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
0749
60.4%
1491
39.6%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
0749
40.3%
.620
33.3%
1491
26.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
0749
40.3%
.620
33.3%
1491
26.4%

static__Infusion_other
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
586 
1.0
 
34

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0586
94.5%
1.034
 
5.5%
2021-04-14T10:05:23.246288image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:23.299119image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0586
94.5%
1.034
 
5.5%

Most occurring characters

ValueCountFrequency (%)
01206
64.8%
.620
33.3%
134
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01206
97.3%
134
 
2.7%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01206
64.8%
.620
33.3%
134
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01206
64.8%
.620
33.3%
134
 
1.8%

static__Oligurie_False
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
1.0
571 
0.0
 
49

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0
ValueCountFrequency (%)
1.0571
92.1%
0.049
 
7.9%
2021-04-14T10:05:23.430547image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:23.483302image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0571
92.1%
0.049
 
7.9%

Most occurring characters

ValueCountFrequency (%)
0669
36.0%
.620
33.3%
1571
30.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
0669
54.0%
1571
46.0%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
0669
36.0%
.620
33.3%
1571
30.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
0669
36.0%
.620
33.3%
1571
30.7%

static__Oligurie_True
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
605 
1.0
 
15

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0605
97.6%
1.015
 
2.4%
2021-04-14T10:05:23.621361image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:23.674360image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0605
97.6%
1.015
 
2.4%

Most occurring characters

ValueCountFrequency (%)
01225
65.9%
.620
33.3%
115
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01225
98.8%
115
 
1.2%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01225
65.9%
.620
33.3%
115
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01225
65.9%
.620
33.3%
115
 
0.8%

static__Oligurie_other
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
586 
1.0
 
34

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0586
94.5%
1.034
 
5.5%
2021-04-14T10:05:23.934510image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:23.987503image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0586
94.5%
1.034
 
5.5%

Most occurring characters

ValueCountFrequency (%)
01206
64.8%
.620
33.3%
134
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01206
97.3%
134
 
2.7%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01206
64.8%
.620
33.3%
134
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01206
64.8%
.620
33.3%
134
 
1.8%

static__SIRSCritHeartRate_False
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
514 
1.0
106 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row1.0
5th row0.0
ValueCountFrequency (%)
0.0514
82.9%
1.0106
 
17.1%
2021-04-14T10:05:24.133760image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:24.186556image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0514
82.9%
1.0106
 
17.1%

Most occurring characters

ValueCountFrequency (%)
01134
61.0%
.620
33.3%
1106
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01134
91.5%
1106
 
8.5%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01134
61.0%
.620
33.3%
1106
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01134
61.0%
.620
33.3%
1106
 
5.7%

static__SIRSCritHeartRate_True
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
1.0
480 
0.0
140 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0
ValueCountFrequency (%)
1.0480
77.4%
0.0140
 
22.6%
2021-04-14T10:05:24.325437image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:24.378215image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0480
77.4%
0.0140
 
22.6%

Most occurring characters

ValueCountFrequency (%)
0760
40.9%
.620
33.3%
1480
25.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
0760
61.3%
1480
38.7%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
0760
40.9%
.620
33.3%
1480
25.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
0760
40.9%
.620
33.3%
1480
25.8%

static__SIRSCritHeartRate_other
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
586 
1.0
 
34

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0586
94.5%
1.034
 
5.5%
2021-04-14T10:05:24.513508image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:24.566303image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0586
94.5%
1.034
 
5.5%

Most occurring characters

ValueCountFrequency (%)
01206
64.8%
.620
33.3%
134
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01206
97.3%
134
 
2.7%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01206
64.8%
.620
33.3%
134
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01206
64.8%
.620
33.3%
134
 
1.8%

static__SIRSCritLeucos_False
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
1.0
559 
0.0
61 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0
ValueCountFrequency (%)
1.0559
90.2%
0.061
 
9.8%
2021-04-14T10:05:24.698523image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:24.751502image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0559
90.2%
0.061
 
9.8%

Most occurring characters

ValueCountFrequency (%)
0681
36.6%
.620
33.3%
1559
30.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
0681
54.9%
1559
45.1%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
0681
36.6%
.620
33.3%
1559
30.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
0681
36.6%
.620
33.3%
1559
30.1%

static__SIRSCritLeucos_True
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
593 
1.0
 
27

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0593
95.6%
1.027
 
4.4%
2021-04-14T10:05:24.889532image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:24.942461image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0593
95.6%
1.027
 
4.4%

Most occurring characters

ValueCountFrequency (%)
01213
65.2%
.620
33.3%
127
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01213
97.8%
127
 
2.2%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01213
65.2%
.620
33.3%
127
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01213
65.2%
.620
33.3%
127
 
1.5%

static__SIRSCritLeucos_other
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
586 
1.0
 
34

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0586
94.5%
1.034
 
5.5%
2021-04-14T10:05:25.077847image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:25.130723image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0586
94.5%
1.034
 
5.5%

Most occurring characters

ValueCountFrequency (%)
01206
64.8%
.620
33.3%
134
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01206
97.3%
134
 
2.7%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01206
64.8%
.620
33.3%
134
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01206
64.8%
.620
33.3%
134
 
1.8%

static__SIRSCritTachypnea_False
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
402 
1.0
218 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0402
64.8%
1.0218
35.2%
2021-04-14T10:05:25.273567image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:25.326442image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0402
64.8%
1.0218
35.2%

Most occurring characters

ValueCountFrequency (%)
01022
54.9%
.620
33.3%
1218
 
11.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01022
82.4%
1218
 
17.6%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01022
54.9%
.620
33.3%
1218
 
11.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01022
54.9%
.620
33.3%
1218
 
11.7%

static__SIRSCritTachypnea_True
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
1.0
368 
0.0
252 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row1.0
5th row1.0
ValueCountFrequency (%)
1.0368
59.4%
0.0252
40.6%
2021-04-14T10:05:25.461892image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:25.514947image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0368
59.4%
0.0252
40.6%

Most occurring characters

ValueCountFrequency (%)
0872
46.9%
.620
33.3%
1368
19.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
0872
70.3%
1368
29.7%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
0872
46.9%
.620
33.3%
1368
19.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
0872
46.9%
.620
33.3%
1368
19.8%

static__SIRSCritTachypnea_other
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
586 
1.0
 
34

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0586
94.5%
1.034
 
5.5%
2021-04-14T10:05:25.653296image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:25.707917image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0586
94.5%
1.034
 
5.5%

Most occurring characters

ValueCountFrequency (%)
01206
64.8%
.620
33.3%
134
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01206
97.3%
134
 
2.7%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01206
64.8%
.620
33.3%
134
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01206
64.8%
.620
33.3%
134
 
1.8%

static__SIRSCritTemperature_False
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
518 
1.0
102 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0518
83.5%
1.0102
 
16.5%
2021-04-14T10:05:25.840478image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:25.894010image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0518
83.5%
1.0102
 
16.5%

Most occurring characters

ValueCountFrequency (%)
01138
61.2%
.620
33.3%
1102
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01138
91.8%
1102
 
8.2%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01138
61.2%
.620
33.3%
1102
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01138
61.2%
.620
33.3%
1102
 
5.5%

static__SIRSCritTemperature_True
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
1.0
484 
0.0
136 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0
ValueCountFrequency (%)
1.0484
78.1%
0.0136
 
21.9%
2021-04-14T10:05:26.180092image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:26.233336image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0484
78.1%
0.0136
 
21.9%

Most occurring characters

ValueCountFrequency (%)
0756
40.6%
.620
33.3%
1484
26.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
0756
61.0%
1484
39.0%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
0756
40.6%
.620
33.3%
1484
26.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
0756
40.6%
.620
33.3%
1484
26.0%

static__SIRSCritTemperature_other
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
586 
1.0
 
34

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0586
94.5%
1.034
 
5.5%
2021-04-14T10:05:26.370841image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:26.424698image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0586
94.5%
1.034
 
5.5%

Most occurring characters

ValueCountFrequency (%)
01206
64.8%
.620
33.3%
134
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01206
97.3%
134
 
2.7%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01206
64.8%
.620
33.3%
134
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01206
64.8%
.620
33.3%
134
 
1.8%

static__SIRSCriteria2OrMore_False
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
549 
1.0
71 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0549
88.5%
1.071
 
11.5%
2021-04-14T10:05:26.556593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:26.609412image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0549
88.5%
1.071
 
11.5%

Most occurring characters

ValueCountFrequency (%)
01169
62.8%
.620
33.3%
171
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01169
94.3%
171
 
5.7%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01169
62.8%
.620
33.3%
171
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01169
62.8%
.620
33.3%
171
 
3.8%

static__SIRSCriteria2OrMore_True
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
1.0
515 
0.0
105 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0
ValueCountFrequency (%)
1.0515
83.1%
0.0105
 
16.9%
2021-04-14T10:05:26.742054image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:26.794987image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0515
83.1%
0.0105
 
16.9%

Most occurring characters

ValueCountFrequency (%)
0725
39.0%
.620
33.3%
1515
27.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
0725
58.5%
1515
41.5%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
0725
39.0%
.620
33.3%
1515
27.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
0725
39.0%
.620
33.3%
1515
27.7%

static__SIRSCriteria2OrMore_other
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
586 
1.0
 
34

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0586
94.5%
1.034
 
5.5%
2021-04-14T10:05:26.930925image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:26.983949image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0586
94.5%
1.034
 
5.5%

Most occurring characters

ValueCountFrequency (%)
01206
64.8%
.620
33.3%
134
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01206
97.3%
134
 
2.7%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01206
64.8%
.620
33.3%
134
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01206
64.8%
.620
33.3%
134
 
1.8%

index__CRP_0
Real number (ℝ≥0)

ZEROS

Distinct15
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.841935484
Minimum0
Maximum342
Zeros606
Zeros (%)97.7%
Memory size5.0 KiB
2021-04-14T10:05:27.035984image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum342
Range342
Interquartile range (IQR)0

Descriptive statistics

Standard deviation22.86451313
Coefficient of variation (CV)8.045401896
Kurtosis120.6805162
Mean2.841935484
Median Absolute Deviation (MAD)0
Skewness10.2274801
Sum1762
Variance522.7859607
MonotocityNot monotonic
2021-04-14T10:05:27.112811image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0606
97.7%
3421
 
0.2%
2131
 
0.2%
2611
 
0.2%
1061
 
0.2%
1121
 
0.2%
1021
 
0.2%
1041
 
0.2%
821
 
0.2%
1361
 
0.2%
Other values (5)5
 
0.8%
ValueCountFrequency (%)
0606
97.7%
111
 
0.2%
141
 
0.2%
721
 
0.2%
811
 
0.2%
ValueCountFrequency (%)
3421
0.2%
2611
0.2%
2131
0.2%
1361
0.2%
1261
0.2%

index__LacticAcid_0
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
620 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0620
100.0%
2021-04-14T10:05:27.276807image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:27.328601image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0620
100.0%

Most occurring characters

ValueCountFrequency (%)
01240
66.7%
.620
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01240
100.0%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01240
66.7%
.620
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01240
66.7%
.620
33.3%

index__Leucocytes_0
Real number (ℝ≥0)

ZEROS

Distinct12
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2441935484
Minimum0
Maximum21.4
Zeros609
Zeros (%)98.2%
Memory size5.0 KiB
2021-04-14T10:05:27.375320image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum21.4
Range21.4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.915715592
Coefficient of variation (CV)7.845070457
Kurtosis72.40797384
Mean0.2441935484
Median Absolute Deviation (MAD)0
Skewness8.36509791
Sum151.4
Variance3.669966231
MonotocityNot monotonic
2021-04-14T10:05:27.448177image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0609
98.2%
7.71
 
0.2%
13.31
 
0.2%
13.71
 
0.2%
9.91
 
0.2%
18.71
 
0.2%
10.81
 
0.2%
21.41
 
0.2%
14.91
 
0.2%
18.51
 
0.2%
Other values (2)2
 
0.3%
ValueCountFrequency (%)
0609
98.2%
6.51
 
0.2%
7.71
 
0.2%
9.91
 
0.2%
10.81
 
0.2%
ValueCountFrequency (%)
21.41
0.2%
18.71
0.2%
18.51
0.2%
161
0.2%
14.91
0.2%

index__hour_0
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct24
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.79677419
Minimum0
Maximum23
Zeros12
Zeros (%)1.9%
Memory size5.0 KiB
2021-04-14T10:05:27.526615image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q19
median13
Q317
95-th percentile21
Maximum23
Range23
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.397659031
Coefficient of variation (CV)0.4217984118
Kurtosis-0.5283741718
Mean12.79677419
Median Absolute Deviation (MAD)4
Skewness-0.2216607784
Sum7934
Variance29.13472302
MonotocityNot monotonic
2021-04-14T10:05:27.608486image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
847
 
7.6%
1342
 
6.8%
1541
 
6.6%
1140
 
6.5%
1640
 
6.5%
938
 
6.1%
1037
 
6.0%
1436
 
5.8%
1235
 
5.6%
1933
 
5.3%
Other values (14)231
37.3%
ValueCountFrequency (%)
012
1.9%
16
1.0%
29
1.5%
37
1.1%
412
1.9%
ValueCountFrequency (%)
2311
 
1.8%
2214
2.3%
2120
3.2%
2030
4.8%
1933
5.3%

index__day_0
Real number (ℝ≥0)

Distinct31
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.37580645
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Memory size5.0 KiB
2021-04-14T10:05:27.694050image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median15
Q324
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)16

Descriptive statistics

Standard deviation8.869493295
Coefficient of variation (CV)0.5768473558
Kurtosis-1.226604313
Mean15.37580645
Median Absolute Deviation (MAD)8
Skewness0.05718770731
Sum9533
Variance78.6679113
MonotocityNot monotonic
2021-04-14T10:05:27.783331image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
2730
 
4.8%
229
 
4.7%
2528
 
4.5%
728
 
4.5%
1126
 
4.2%
1625
 
4.0%
123
 
3.7%
1223
 
3.7%
923
 
3.7%
523
 
3.7%
Other values (21)362
58.4%
ValueCountFrequency (%)
123
3.7%
229
4.7%
312
1.9%
418
2.9%
523
3.7%
ValueCountFrequency (%)
3111
 
1.8%
3014
2.3%
2920
3.2%
2814
2.3%
2730
4.8%

index__month_0
Real number (ℝ≥0)

Distinct12
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.15
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Memory size5.0 KiB
2021-04-14T10:05:27.871416image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile11.05
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.154874168
Coefficient of variation (CV)0.5129876696
Kurtosis-1.073473456
Mean6.15
Median Absolute Deviation (MAD)3
Skewness0.1325668749
Sum3813
Variance9.953231018
MonotocityNot monotonic
2021-04-14T10:05:27.947119image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
575
12.1%
370
11.3%
866
10.6%
458
9.4%
1057
9.2%
953
8.5%
651
8.2%
250
8.1%
744
7.1%
138
6.1%
Other values (2)58
9.4%
ValueCountFrequency (%)
138
6.1%
250
8.1%
370
11.3%
458
9.4%
575
12.1%
ValueCountFrequency (%)
1231
5.0%
1127
4.4%
1057
9.2%
953
8.5%
866
10.6%

index__timesincemidnight_0
Real number (ℝ≥0)

HIGH CORRELATION

Distinct484
Distinct (%)78.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean796.9806452
Minimum9
Maximum1438
Zeros0
Zeros (%)0.0%
Memory size5.0 KiB
2021-04-14T10:05:28.041620image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile203.25
Q1563.5
median802
Q31044.5
95-th percentile1297.2
Maximum1438
Range1429
Interquartile range (IQR)481

Descriptive statistics

Standard deviation323.6651767
Coefficient of variation (CV)0.4061142246
Kurtosis-0.5172131052
Mean796.9806452
Median Absolute Deviation (MAD)241.5
Skewness-0.2240357107
Sum494128
Variance104759.1466
MonotocityNot monotonic
2021-04-14T10:05:28.146728image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5504
 
0.6%
9314
 
0.6%
6474
 
0.6%
7994
 
0.6%
12243
 
0.5%
5143
 
0.5%
10043
 
0.5%
5703
 
0.5%
4953
 
0.5%
6063
 
0.5%
Other values (474)586
94.5%
ValueCountFrequency (%)
92
0.3%
111
0.2%
131
0.2%
151
0.2%
221
0.2%
ValueCountFrequency (%)
14381
0.2%
14371
0.2%
14201
0.2%
14181
0.2%
13981
0.2%

index__timesincelast_0
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
620 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0620
100.0%
2021-04-14T10:05:28.461354image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:28.514054image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0620
100.0%

Most occurring characters

ValueCountFrequency (%)
01240
66.7%
.620
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01240
100.0%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01240
66.7%
.620
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01240
66.7%
.620
33.3%

index__timesincestart_0
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
620 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0620
100.0%
2021-04-14T10:05:28.642099image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:28.694957image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0620
100.0%

Most occurring characters

ValueCountFrequency (%)
01240
66.7%
.620
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01240
100.0%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01240
66.7%
.620
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01240
66.7%
.620
33.3%

index__OrderOfEvent_0
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
1.0
612 
2.0
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0
ValueCountFrequency (%)
1.0612
98.7%
2.08
 
1.3%
2021-04-14T10:05:28.827712image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:28.880804image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0612
98.7%
2.08
 
1.3%

Most occurring characters

ValueCountFrequency (%)
.620
33.3%
0620
33.3%
1612
32.9%
28
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
0620
50.0%
1612
49.4%
28
 
0.6%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
.620
33.3%
0620
33.3%
1612
32.9%
28
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
.620
33.3%
0620
33.3%
1612
32.9%
28
 
0.4%

index__openCases_0
Real number (ℝ≥0)

Distinct93
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.2516129
Minimum1
Maximum93
Zeros0
Zeros (%)0.0%
Memory size5.0 KiB
2021-04-14T10:05:28.947780image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile17
Q157
median78
Q384
95-th percentile89
Maximum93
Range92
Interquartile range (IQR)27

Descriptive statistics

Standard deviation23.4058368
Coefficient of variation (CV)0.3480338357
Kurtosis0.04970865902
Mean67.2516129
Median Absolute Deviation (MAD)8
Skewness-1.153368737
Sum41696
Variance547.8331961
MonotocityNot monotonic
2021-04-14T10:05:29.052840image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8440
 
6.5%
8333
 
5.3%
8228
 
4.5%
7827
 
4.4%
7924
 
3.9%
8724
 
3.9%
8123
 
3.7%
8523
 
3.7%
8822
 
3.5%
8022
 
3.5%
Other values (83)354
57.1%
ValueCountFrequency (%)
11
0.2%
21
0.2%
31
0.2%
41
0.2%
51
0.2%
ValueCountFrequency (%)
932
 
0.3%
927
1.1%
919
1.5%
9011
1.8%
8914
2.3%

index__remainingtime_0
Real number (ℝ≥0)

UNIQUE

Distinct620
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3685658.971
Minimum10380
Maximum36488789
Zeros0
Zeros (%)0.0%
Memory size5.0 KiB
2021-04-14T10:05:29.159181image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum10380
5-th percentile174244.85
Q1404198.75
median847820
Q33400612.5
95-th percentile18042464.15
Maximum36488789
Range36478409
Interquartile range (IQR)2996413.75

Descriptive statistics

Standard deviation6341513.776
Coefficient of variation (CV)1.720591576
Kurtosis7.309292146
Mean3685658.971
Median Absolute Deviation (MAD)553736
Skewness2.632681489
Sum2285108562
Variance4.021479697 × 1013
MonotocityNot monotonic
2021-04-14T10:05:29.267110image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4357111
 
0.2%
30856071
 
0.2%
14711441
 
0.2%
2384541
 
0.2%
2740891
 
0.2%
2609481
 
0.2%
5317891
 
0.2%
3160701
 
0.2%
273718451
 
0.2%
12499371
 
0.2%
Other values (610)610
98.4%
ValueCountFrequency (%)
103801
0.2%
116661
0.2%
180001
0.2%
283911
0.2%
294901
0.2%
ValueCountFrequency (%)
364887891
0.2%
364456071
0.2%
344670031
0.2%
342980701
0.2%
334342001
0.2%

index__concept:name_0_CRP
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
606 
1.0
 
14

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0606
97.7%
1.014
 
2.3%
2021-04-14T10:05:29.452188image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:29.505231image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0606
97.7%
1.014
 
2.3%

Most occurring characters

ValueCountFrequency (%)
01226
65.9%
.620
33.3%
114
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01226
98.9%
114
 
1.1%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01226
65.9%
.620
33.3%
114
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01226
65.9%
.620
33.3%
114
 
0.8%

index__concept:name_0_ER Registration
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
1.0
586 
0.0
 
34

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0
ValueCountFrequency (%)
1.0586
94.5%
0.034
 
5.5%
2021-04-14T10:05:29.640321image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:29.693348image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0586
94.5%
0.034
 
5.5%

Most occurring characters

ValueCountFrequency (%)
0654
35.2%
.620
33.3%
1586
31.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
0654
52.7%
1586
47.3%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
0654
35.2%
.620
33.3%
1586
31.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
0654
35.2%
.620
33.3%
1586
31.5%

index__concept:name_0_ER Sepsis Triage
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
616 
1.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0616
99.4%
1.04
 
0.6%
2021-04-14T10:05:29.831800image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:29.884700image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0616
99.4%
1.04
 
0.6%

Most occurring characters

ValueCountFrequency (%)
01236
66.5%
.620
33.3%
14
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01236
99.7%
14
 
0.3%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01236
66.5%
.620
33.3%
14
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01236
66.5%
.620
33.3%
14
 
0.2%

index__concept:name_0_ER Triage
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
617 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0617
99.5%
1.03
 
0.5%
2021-04-14T10:05:30.022918image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:30.075805image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0617
99.5%
1.03
 
0.5%

Most occurring characters

ValueCountFrequency (%)
01237
66.5%
.620
33.3%
13
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01237
99.8%
13
 
0.2%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01237
66.5%
.620
33.3%
13
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01237
66.5%
.620
33.3%
13
 
0.2%

index__concept:name_0_IV Liquid
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
610 
1.0
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0610
98.4%
1.010
 
1.6%
2021-04-14T10:05:30.214330image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:30.267221image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0610
98.4%
1.010
 
1.6%

Most occurring characters

ValueCountFrequency (%)
01230
66.1%
.620
33.3%
110
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01230
99.2%
110
 
0.8%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01230
66.1%
.620
33.3%
110
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01230
66.1%
.620
33.3%
110
 
0.5%

index__concept:name_0_Leucocytes
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
617 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0617
99.5%
1.03
 
0.5%
2021-04-14T10:05:30.531260image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:30.584349image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0617
99.5%
1.03
 
0.5%

Most occurring characters

ValueCountFrequency (%)
01237
66.5%
.620
33.3%
13
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01237
99.8%
13
 
0.2%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01237
66.5%
.620
33.3%
13
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01237
66.5%
.620
33.3%
13
 
0.2%

index__org:group_0_A
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
1.0
571 
0.0
 
49

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0
ValueCountFrequency (%)
1.0571
92.1%
0.049
 
7.9%
2021-04-14T10:05:30.716164image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:30.769085image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0571
92.1%
0.049
 
7.9%

Most occurring characters

ValueCountFrequency (%)
0669
36.0%
.620
33.3%
1571
30.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
0669
54.0%
1571
46.0%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
0669
36.0%
.620
33.3%
1571
30.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
0669
36.0%
.620
33.3%
1571
30.7%

index__org:group_0_B
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
603 
1.0
 
17

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0603
97.3%
1.017
 
2.7%
2021-04-14T10:05:30.907178image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:30.960125image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0603
97.3%
1.017
 
2.7%

Most occurring characters

ValueCountFrequency (%)
01223
65.8%
.620
33.3%
117
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01223
98.6%
117
 
1.4%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01223
65.8%
.620
33.3%
117
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01223
65.8%
.620
33.3%
117
 
0.9%

index__org:group_0_C
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
617 
1.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0617
99.5%
1.03
 
0.5%
2021-04-14T10:05:31.098171image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:31.151126image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0617
99.5%
1.03
 
0.5%

Most occurring characters

ValueCountFrequency (%)
01237
66.5%
.620
33.3%
13
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01237
99.8%
13
 
0.2%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01237
66.5%
.620
33.3%
13
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01237
66.5%
.620
33.3%
13
 
0.2%

index__org:group_0_L
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size5.0 KiB
0.0
591 
1.0
 
29

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1860
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.0591
95.3%
1.029
 
4.7%
2021-04-14T10:05:31.286283image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-04-14T10:05:31.339274image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0591
95.3%
1.029
 
4.7%

Most occurring characters

ValueCountFrequency (%)
01211
65.1%
.620
33.3%
129
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1240
66.7%
Other Punctuation620
33.3%

Most frequent character per category

ValueCountFrequency (%)
01211
97.7%
129
 
2.3%
ValueCountFrequency (%)
.620
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1860
100.0%

Most frequent character per script

ValueCountFrequency (%)
01211
65.1%
.620
33.3%
129
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1860
100.0%

Most frequent character per block

ValueCountFrequency (%)
01211
65.1%
.620
33.3%
129
 
1.6%

Interactions

2021-04-14T10:04:57.101820image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:04:57.302981image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:04:57.395759image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:04:57.488030image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:04:57.584754image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:04:57.679283image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:04:57.779715image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:04:57.872426image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:04:57.974035image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:04:58.196985image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:04:58.288510image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:04:58.379107image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:04:58.474061image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:04:58.567123image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:04:58.666399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:04:58.757968image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:04:58.858050image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:04:58.946992image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:04:59.036201image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:04:59.118299image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:04:59.204899image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:04:59.289676image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:04:59.380528image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:04:59.463717image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:04:59.555575image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:04:59.645082image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:04:59.734128image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:04:59.816518image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:04:59.903152image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:04:59.987540image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:05:00.078210image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:05:00.161155image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:05:00.252765image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:05:00.348021image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:05:00.443485image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:05:00.532281image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:05:00.620578image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:05:00.711687image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:05:00.808456image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:05:00.897459image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:05:00.995203image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:05:01.213677image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:05:01.306393image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:05:01.391874image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:05:01.476681image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:05:01.566040image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:05:01.659335image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:05:01.744661image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:05:01.838892image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:05:01.937671image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:05:02.036502image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:05:02.128891image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:05:02.220620image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:05:02.317095image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:05:02.411149image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:05:02.503570image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:05:02.604695image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:05:02.693599image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:05:02.782720image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:05:02.865358image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:05:02.947261image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:05:03.033631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:05:03.117865image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:05:03.208158image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:05:03.299389image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:05:03.400094image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:05:03.500756image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:05:03.594840image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:05:03.688355image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:05:03.786275image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:05:03.882431image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-04-14T10:05:03.984246image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-04-14T10:05:31.535738image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-04-14T10:05:33.802303image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-04-14T10:05:36.023154image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-04-14T10:05:38.370847image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-04-14T10:05:40.509820image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-04-14T10:05:04.538656image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-04-14T10:05:09.689642image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

static__Agestatic__Diagnose_Bstatic__Diagnose_Cstatic__Diagnose_Dstatic__Diagnose_Estatic__Diagnose_Gstatic__Diagnose_Hstatic__Diagnose_Kstatic__Diagnose_Nstatic__Diagnose_Qstatic__Diagnose_Rstatic__Diagnose_Sstatic__Diagnose_otherstatic__DiagnosticArtAstrup_Falsestatic__DiagnosticArtAstrup_Truestatic__DiagnosticArtAstrup_otherstatic__DiagnosticBlood_Falsestatic__DiagnosticBlood_Truestatic__DiagnosticBlood_otherstatic__DiagnosticECG_Falsestatic__DiagnosticECG_Truestatic__DiagnosticECG_otherstatic__DiagnosticIC_Falsestatic__DiagnosticIC_Truestatic__DiagnosticIC_otherstatic__DiagnosticLacticAcid_Falsestatic__DiagnosticLacticAcid_Truestatic__DiagnosticLacticAcid_otherstatic__DiagnosticLiquor_Falsestatic__DiagnosticLiquor_otherstatic__DiagnosticOther_Falsestatic__DiagnosticOther_otherstatic__DiagnosticSputum_Falsestatic__DiagnosticSputum_Truestatic__DiagnosticSputum_otherstatic__DiagnosticUrinaryCulture_Falsestatic__DiagnosticUrinaryCulture_Truestatic__DiagnosticUrinaryCulture_otherstatic__DiagnosticUrinarySediment_Falsestatic__DiagnosticUrinarySediment_Truestatic__DiagnosticUrinarySediment_otherstatic__DiagnosticXthorax_Falsestatic__DiagnosticXthorax_Truestatic__DiagnosticXthorax_otherstatic__DisfuncOrg_Falsestatic__DisfuncOrg_Truestatic__DisfuncOrg_otherstatic__Hypotensie_Falsestatic__Hypotensie_Truestatic__Hypotensie_otherstatic__Hypoxie_Falsestatic__Hypoxie_Truestatic__Hypoxie_otherstatic__InfectionSuspected_Falsestatic__InfectionSuspected_Truestatic__InfectionSuspected_otherstatic__Infusion_Falsestatic__Infusion_Truestatic__Infusion_otherstatic__Oligurie_Falsestatic__Oligurie_Truestatic__Oligurie_otherstatic__SIRSCritHeartRate_Falsestatic__SIRSCritHeartRate_Truestatic__SIRSCritHeartRate_otherstatic__SIRSCritLeucos_Falsestatic__SIRSCritLeucos_Truestatic__SIRSCritLeucos_otherstatic__SIRSCritTachypnea_Falsestatic__SIRSCritTachypnea_Truestatic__SIRSCritTachypnea_otherstatic__SIRSCritTemperature_Falsestatic__SIRSCritTemperature_Truestatic__SIRSCritTemperature_otherstatic__SIRSCriteria2OrMore_Falsestatic__SIRSCriteria2OrMore_Truestatic__SIRSCriteria2OrMore_otherindex__CRP_0index__LacticAcid_0index__Leucocytes_0index__hour_0index__day_0index__month_0index__timesincemidnight_0index__timesincelast_0index__timesincestart_0index__OrderOfEvent_0index__openCases_0index__remainingtime_0index__concept:name_0_CRPindex__concept:name_0_ER Registrationindex__concept:name_0_ER Sepsis Triageindex__concept:name_0_ER Triageindex__concept:name_0_IV Liquidindex__concept:name_0_Leucocytesindex__org:group_0_Aindex__org:group_0_Bindex__org:group_0_Cindex__org:group_0_L
085.00.00.00.00.00.00.00.00.00.00.00.01.00.01.00.00.01.00.00.01.00.00.01.00.00.01.00.01.00.01.00.01.00.00.00.01.00.00.01.00.00.01.00.00.01.00.00.01.00.01.00.00.00.01.00.00.01.00.01.00.00.00.01.00.01.00.00.00.01.00.00.01.00.00.01.00.00.00.00.09.022.010.0555.00.00.01.081.0968359.00.01.00.00.00.00.01.00.00.00.0
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